DE RNA
# month 3 comparisons
Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Meth_vs_Bup.Nalo_3<-FindMarkers(results_cyto, "Methadone_3","Bup.Nalo_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
# month 0 comparisons
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Meth_vs_Bup.Nalo_0<-FindMarkers(results_cyto, "Methadone_0","Bup.Nalo_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
todo<-list(Meth_vs_Nal_3,Meth_vs_Bup.Nalo_3,Bup.Nalo_vs_Nal_3,Meth_vs_Nal_0,Meth_vs_Bup.Nalo_0,Bup.Nalo_vs_Nal_0 )
names(todo)<-c("Meth_vs_Nal_3","Meth_vs_Bup.Nalo_3","Bup.Nalo_vs_Nal_3","Meth_vs_Nal_0","Meth_vs_Bup.Nalo_0","Bup.Nalo_vs_Nal_0")
for(i in 1:length(todo)){
todo[[i]]$p_val_adj<-p.adjust( todo[[i]]$p_val, "BH")
print(VolPlot( todo[[i]], Title = names(todo)[[i]]))
}
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 205 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/A_F.pdf", width = 16, height = 9)
for(i in 1:length(todo)){
print(VolPlot(todo[[i]], Title = names(todo)[[i]]))
}
## Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 204 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
dev.off()->.
#subset to just DE genes for these tables to avoid them being too unwieldy
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Nal_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Bup.Nalo_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Bup.Nalo_vs_Nal_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Nal_0, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Bup.Nalo_0, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Bup.Nalo_vs_Nal_0, p_val_adj<0.01))
GSEA
#grabbing hallmark as well as the curated, immune
m_df_H<- msigdbr(species = "Homo sapiens", category = "H")
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C2"), m_df_H)
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C7"), m_df_H)
fgsea_sets<- m_df_H %>% split(x = .$gene_symbol, f = .$gs_name)
# month 3 comparisons
Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Meth_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Methadone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
# month 0 comparisons
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Meth_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Methadone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
todo<-list(Meth_vs_Nal_3,Bup.Nalo_vs_Meth_3,Bup.Nalo_vs_Nal_3,Meth_vs_Nal_0,Bup.Nalo_vs_Meth_0,Bup.Nalo_vs_Nal_0 )
names(todo)<-c("Meth_vs_Nal_3","Bup.Nalo_vs_Meth_3","Bup.Nalo_vs_Nal_3","Meth_vs_Nal_0","Bup.Nalo_vs_Meth_0","Bup.Nalo_vs_Nal_0")
GSEAres<-list()
for (i in 1:length(todo)){
GSEAres[[i]]<-GSEA(todo[[i]], genesets = fgsea_sets)
GSEAres[[i]]<-GSEATable(GSEAwrap_out =GSEAres[[i]], gmt = fgsea_sets, name = names(todo)[[i]] )
}
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.32% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.72% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.74% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.09% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (6.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
GSEAres<-GSEAbig(listofGSEAtables = GSEAres)
to_plot<-c("HALLMARK_TNFA_SIGNALING_VIA_NFKB","BOSCO_INTERFERON_INDUCED_ANTIVIRAL_MODULE","GSE5960_TH1_VS_ANERGIC_TH1_UP")
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(GSEAres, padj<0.001))
## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html
GSEAEnrichmentPlotComparison(to_plot[1], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[2], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[3], GSEAres, "BOTH", cols = IsleofDogs1)
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/G_I.pdf", width = 16, height = 9)
GSEAEnrichmentPlotComparison(to_plot[1], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[2], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[3], GSEAres, "BOTH", cols = IsleofDogs1)
dev.off()
## png
## 2
DE CHROMVar
DefaultAssay(results_cyto)<-"chromvar"
# month 3 comparisons
Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", mean.fxn = rowMeans)
Meth_vs_Bup.Nalo_3<-FindMarkers(results_cyto, "Methadone_3","Bup.Nalo_3", mean.fxn = rowMeans)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", mean.fxn = rowMeans)
# month 0 comparisons
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", mean.fxn = rowMeans)
Meth_vs_Bup.Nalo_0<-FindMarkers(results_cyto, "Methadone_0","Bup.Nalo_0", mean.fxn = rowMeans)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", mean.fxn = rowMeans)
Meth_vs_Nal_3<-FixMotifID(Meth_vs_Nal_3, results_cyto)
Meth_vs_Bup.Nalo_3<-FixMotifID(Meth_vs_Bup.Nalo_3, results_cyto)
Bup.Nalo_vs_Nal_3<-FixMotifID(Bup.Nalo_vs_Nal_3, results_cyto)
Meth_vs_Nal_0<-FixMotifID(Meth_vs_Nal_0, results_cyto)
Meth_vs_Bup.Nalo_0<-FixMotifID(Meth_vs_Bup.Nalo_0, results_cyto)
Bup.Nalo_vs_Nal_0<-FixMotifID(Bup.Nalo_vs_Nal_0, results_cyto)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Nal_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Bup.Nalo_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Bup.Nalo_vs_Nal_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Nal_0)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Bup.Nalo_0)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Bup.Nalo_vs_Nal_0)
Idents(results_cyto)<-factor(Idents(results_cyto),levels = c("Methadone_0","Bup.Nalo_0","Naltrexone_0","Methadone_3", "Bup.Nalo_3", "Naltrexone_3"))
BottleRocket3<-c("Methadone_0" = "#3b4357", "Bup.Nalo_0" = "#cb2314","Naltrexone_0"= "#fad510",
"Methadone_3" = "#273046", "Bup.Nalo_3" = "#792a2d","Naltrexone_3"= "#e37c12")
VlnPlot(results_cyto, "MA0101.1", pt.size = 0.1 )+ggtitle("RELA")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA1143.1", pt.size = 0.1 )+ggtitle("FOSL1::JUND")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA0605.2", pt.size = 0.1 )+ggtitle("ATF3")+scale_fill_manual(values=BottleRocket3)
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/J_L.pdf", width = 16, height = 9)
VlnPlot(results_cyto, "MA0101.1", pt.size = 0.1 )+ggtitle("RELA")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA1143.1", pt.size = 0.1 )+ggtitle("FOSL1::JUND")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA0605.2", pt.size = 0.1 )+ggtitle("ATF3")+scale_fill_manual(values=BottleRocket3)
dev.off()
## png
## 2
devtools::session_info()
## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1
## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.8 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype C
## tz Etc/UTC
## date 2023-07-27
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
## - Packages -------------------------------------------------------------------
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.2.0)
## babelgene 22.9 2022-09-29 [1] CRAN (R 4.2.0)
## backports 1.4.1 2021-12-13 [2] CRAN (R 4.2.0)
## beeswarm 0.4.0 2021-06-01 [2] CRAN (R 4.2.0)
## BiocGenerics 0.44.0 2022-11-01 [1] Bioconductor
## BiocParallel 1.32.6 2023-03-17 [1] Bioconductor
## Biostrings 2.66.0 2022-11-01 [1] Bioconductor
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.2.0)
## broom 1.0.4 2023-03-11 [1] CRAN (R 4.2.0)
## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## car 3.1-2 2023-03-30 [1] CRAN (R 4.2.0)
## carData 3.0-5 2022-01-06 [2] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [2] CRAN (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [2] CRAN (R 4.2.0)
## crosstalk 1.2.0 2021-11-04 [2] CRAN (R 4.2.0)
## data.table 1.14.8 2023-02-17 [2] CRAN (R 4.2.0)
## DBI 1.1.3 2022-06-18 [2] CRAN (R 4.2.0)
## deldir 1.0-6 2021-10-23 [2] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.31 2022-12-11 [2] CRAN (R 4.2.0)
## dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
## DT 0.28 2023-05-18 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.2.0)
## evaluate 0.20 2023-01-17 [2] CRAN (R 4.2.0)
## fansi 1.0.4 2023-01-22 [2] CRAN (R 4.2.0)
## farver 2.1.1 2022-07-06 [2] CRAN (R 4.2.0)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
## fastmatch 1.1-3 2021-07-23 [2] CRAN (R 4.2.0)
## fgsea * 1.24.0 2022-11-01 [1] Bioconductor
## fitdistrplus 1.1-8 2022-03-10 [2] CRAN (R 4.2.0)
## fs 1.6.1 2023-02-06 [2] CRAN (R 4.2.0)
## future 1.32.0 2023-03-07 [1] CRAN (R 4.2.0)
## future.apply 1.10.0 2022-11-05 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [2] CRAN (R 4.2.0)
## GenomeInfoDb 1.34.9 2023-02-02 [1] Bioconductor
## GenomeInfoDbData 1.2.9 2023-03-17 [1] Bioconductor
## GenomicRanges 1.50.2 2022-12-16 [1] Bioconductor
## ggbeeswarm 0.7.2 2023-04-29 [1] CRAN (R 4.2.0)
## ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.2.0)
## ggpubr * 0.6.0 2023-02-10 [1] CRAN (R 4.2.0)
## ggrastr 1.0.1 2021-12-08 [1] CRAN (R 4.2.0)
## ggrepel * 0.9.3 2023-02-03 [1] CRAN (R 4.2.0)
## ggridges 0.5.4 2022-09-26 [1] CRAN (R 4.2.0)
## ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.2.0)
## globals 0.16.2 2022-11-21 [1] CRAN (R 4.2.0)
## glue 1.6.2 2022-02-24 [2] CRAN (R 4.2.0)
## goftest 1.2-3 2021-10-07 [2] CRAN (R 4.2.0)
## gridExtra * 2.3 2017-09-09 [2] CRAN (R 4.2.0)
## gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0)
## highr 0.10 2022-12-22 [1] CRAN (R 4.2.0)
## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
## httpuv 1.6.9 2023-02-14 [1] CRAN (R 4.2.0)
## httr 1.4.5 2023-02-24 [1] CRAN (R 4.2.0)
## ica 1.0-3 2022-07-08 [2] CRAN (R 4.2.0)
## igraph 1.4.2 2023-04-07 [1] CRAN (R 4.2.0)
## IRanges 2.32.0 2022-11-01 [1] Bioconductor
## irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.2.0)
## jsonlite 1.8.4 2022-12-06 [2] CRAN (R 4.2.0)
## KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.0)
## knitr 1.42 2023-01-25 [1] CRAN (R 4.2.0)
## labeling 0.4.2 2020-10-20 [2] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [2] CRAN (R 4.2.0)
## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.0)
## lazyeval 0.2.2 2019-03-15 [2] CRAN (R 4.2.0)
## leiden 0.4.3 2022-09-10 [1] CRAN (R 4.2.0)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
## limma 3.54.2 2023-02-28 [1] Bioconductor
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## lmtest 0.9-40 2022-03-21 [2] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [2] CRAN (R 4.2.0)
## MASS 7.3-59 2023-04-21 [1] CRAN (R 4.2.0)
## Matrix 1.5-4 2023-04-04 [1] CRAN (R 4.2.0)
## matrixStats 0.63.0 2022-11-18 [2] CRAN (R 4.2.0)
## memoise 2.0.1 2021-11-26 [2] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [2] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [2] CRAN (R 4.2.0)
## msigdbr * 7.5.1 2022-03-30 [1] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [2] CRAN (R 4.2.0)
## nlme 3.1-162 2023-01-31 [1] CRAN (R 4.2.0)
## parallelly 1.35.0 2023-03-23 [1] CRAN (R 4.2.0)
## patchwork 1.1.2 2022-08-19 [1] CRAN (R 4.2.0)
## pbapply 1.7-0 2023-01-13 [1] CRAN (R 4.2.0)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] CRAN (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.2.0)
## processx 3.8.1 2023-04-18 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## progressr 0.13.0 2023-01-10 [1] CRAN (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.2.0)
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.2.0)
## RANN 2.6.1 2019-01-08 [2] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [2] CRAN (R 4.2.0)
## Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.2.0)
## RcppRoll 0.3.0 2018-06-05 [2] CRAN (R 4.2.0)
## RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
## remotes 2.4.2 2021-11-30 [2] CRAN (R 4.2.0)
## reshape2 * 1.4.4 2020-04-09 [2] CRAN (R 4.2.0)
## reticulate 1.28 2023-01-27 [1] CRAN (R 4.2.0)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.22 2023-06-01 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [2] CRAN (R 4.2.0)
## Rsamtools 2.14.0 2022-11-01 [1] Bioconductor
## rstatix 0.7.2 2023-02-01 [1] CRAN (R 4.2.0)
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
## Rtsne 0.16 2022-04-17 [2] CRAN (R 4.2.0)
## S4Vectors 0.36.2 2023-02-26 [1] Bioconductor
## sass 0.4.5 2023-01-24 [1] CRAN (R 4.2.0)
## scales * 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.0)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.0)
## Seurat * 4.3.0 2022-11-18 [1] CRAN (R 4.2.0)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.0)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.0)
## Signac * 1.9.0 2022-12-08 [1] CRAN (R 4.2.0)
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.2.0)
## spatstat.data 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.explore 3.1-0 2023-03-14 [1] CRAN (R 4.2.0)
## spatstat.geom 3.1-0 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.random 3.1-4 2023-03-13 [1] CRAN (R 4.2.0)
## spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.utils 3.0-2 2023-03-11 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## vipor 0.4.5 2017-03-22 [2] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
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